In the recent decade, we have witnessed widespread of deep neural networks and their applications. With the evolution of consumer electronics, the range of applicable devices for such deep neural networks is expanding as well to personal, mobile, or even wearable devices. The new challenge of such systems is to efficiently manage data streams between sensors (cameras, mics, radars, lidars, and so on), media filters, neural network models and their post processors, and applications. In order to tackle the challenge with less effort and more effect, we propose to implement general neural network supporting filters for Gstreamer, which is actively developed and tested at https://github.com/nnsuite/nnstreamer

With NNStreamer, neural network developers may easily configure streams with various sensors and models and execute the streams with high efficiency. Besides, media stream developers can now use deep neural networks as yet another media filters with much less efforts.

MyungJoo Ham, Ph.D., has been working in Samsung Electronics as a software developer after receiving the Ph.D. degree from University of Illinois in 2009. Recently, he has been developing development environment and software platform for on-device AI projects varying from autonomous driving systems to consumer electronics in AI Center of Samsung. Before joining AI Center, he had worked mostly on Tizen as an architect and lead developer with responsibilities on Linux kernel, system frameworks, base libraries, .NET runtime, and so on. He has been a maintainer of a couple of Linux kernel subsystems and contributor of a few other open source projects.

Visibility: This media is published

Add to notification list

My favoritesWith attachmentsUnansweredNewMy annotations

Reset filters

Get notified of changes by email

In the recent decade, we have witnessed widespread of deep neural networks and their applications. With the evolution of consumer electronics, the range of applicable devices for such deep neural networks is expanding as well to personal, mobile, or even wearable devices. The new challenge of such systems is to efficiently manage data streams between sensors (cameras, mics, radars, lidars, and so on), media filters, neural network models and their post processors, and applications. In order to tackle the challenge with less effort and more effect, we propose to implement general neural network supporting filters for Gstreamer, which is actively developed and tested at https://github.com/nnsuite/nnstreamer

With NNStreamer, neural network developers may easily configure streams with various sensors and models and execute the streams with high efficiency. Besides, media stream developers can now use deep neural networks as yet another media filters with much less efforts.

MyungJoo Ham, Ph.D., has been working in Samsung Electronics as a software developer after receiving the Ph.D. degree from University of Illinois in 2009. Recently, he has been developing development environment and software platform for on-device AI projects varying from autonomous driving systems to consumer electronics in AI Center of Samsung. Before joining AI Center, he had worked mostly on Tizen as an architect and lead developer with responsibilities on Linux kernel, system frameworks, base libraries, .NET runtime, and so on. He has been a maintainer of a couple of Linux kernel subsystems and contributor of a few other open source projects.